Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #7,587 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
Vizhi is a self-reported tool designed to improve supervision of Codex CLI agents by offering a local, hardware- and browser-based interface for managing multiple agent sessions. It allows users to monitor and interact with up to six parallel Codex CLI sessions using either a Logitech keypad or a local browser dashboard.
What changed
The author reports building Vizhi as an extension of their own workflow during the OpenAI 2026 hackathon, aiming to reduce manual intervention in agent workflows by replacing keyboard input with single-key or voice-based actions. The tool was built using Codex CLI and a mix of technologies including C#, JavaScript, Swift, and TypeScript.
Single most important open question
Is there any evidence of real-world usage, adoption, or traction beyond the author’s own development experience?
What The Product Actually Is
The description states that Vizhi is a tool for supervising Codex CLI sessions. It provides two interfaces:
- A Logitech MX Creative Keypad with six LCD keys showing session status (project name, context percentage, Working/Ready state, and waiting status).
- A local browser dashboard, which includes a grid of sessions, screenshot-and-voice context, prompt templates, git shortcuts, and live controls for model/reasoning/mode.
Users can:
- Press Yes or No on the keypad.
- Hold a voice button to speak prompts, which are transcribed locally using Whisper.
- Interact with sessions without needing to switch tabs or type extensively.
The tool runs entirely on localhost, uses fresh tokens per session, and does not require cloud storage, accounts, or admin passwords.
Evidence
- The author states Vizhi shows up to six live Codex CLI sessions.
- It supports both keypad and browser interfaces.
- Voice input is processed locally with Whisper.
- Everything runs on localhost with no cloud dependencies.
Inference The tool appears to be a developer-focused interface for managing AI agent workflows, designed to reduce friction in decision-making during agent execution.
Positioning & Claim Evolution
The author positions Vizhi as a solution to the inefficiency of manual supervision in agent-based coding workflows. The tagline — “Agents write the code now. Your job is deciding.” — reflects this positioning.
Key claims:
- The tool turns Codex CLI supervision into simple, single-press interactions.
- It reduces the need for tab-hunting and keyboard input during agent sessions.
- Vizhi watches agents so users don’t have to.
Evidence
- The author describes a workflow where they spend time tab-hunting when agents pause for permission.
- They state that most decisions are now made with one key press or voice command.
- The tool is built to support the shift from typing code to making single decisions.
Inference Vizhi positions itself as an interface tool for AI agent supervision, not a standalone AI product. It aims to improve human-agent collaboration by streamlining decision-making.
Target Customer & ICP
The description states that Vizhi is built for developers who use Codex CLI and run multiple parallel sessions. The author describes their own workflow as someone who "runs coding agents in the terminal every day."
Evidence
- The tool is designed for users of Codex CLI.
- It targets those who manage multiple agent sessions simultaneously.
- The interface is optimized for single-key or voice-based decision-making.
Inference The target customer likely includes developers or engineers working with AI agents in terminal environments, particularly those managing parallel workflows.
Business Model & Pricing Evidence
There is no evidence of a business model or pricing structure in the description. The author does not mention monetization, subscriptions, licensing, or any commercial offering.
Evidence
- No pricing, revenue, or monetization strategy is described.
- The tool is built for personal use and development, with no indication of a commercial product.
Inference The project appears to be a hackathon prototype or personal tool, not a commercial offering.
Technical & Delivery Signals
The author reports building Vizhi using:
- Codex CLI (with GPT-5.6 and Terra models)
- C#, JavaScript, Swift, TypeScript
- Node service with zero runtime dependencies
- A C# .NET plugin for keypad
- Swift helper for Whisper voice
- JXA hook script for Codex events
The tool is designed to:
- Run entirely on localhost
- Use a fresh token per session
- Support both keypad and browser interfaces
- Process voice input locally with Whisper
- Require no admin password or cloud storage
Evidence
- The stack includes multiple languages and frameworks.
- It uses local processing for voice transcription.
- Installation is automated but requires user consent.
Inference The tool is technically complex, built with a focus on local execution and minimal dependencies. It reflects a developer-centric approach to building tools that integrate with AI workflows.
Traction & Maturity Signals
There is no evidence of traction, customers, or adoption beyond the author’s own experience. The project was submitted as part of a hackathon and has no stated user base or usage metrics.
Evidence
- No revenue, customer data, or usage statistics are provided.
- The tool is described as a weekend project with three tagged releases.
- It was built for personal use during the hackathon.
Inference The project is in an early stage and lacks any evidence of real-world traction or commercial adoption.
Competitive Context
There is no mention of competitors or existing tools in the description. The author does not reference other agent supervision tools, IDE integrations, or terminal-based AI interfaces.
Evidence
- No competitive landscape or comparison to existing tools is described.
- The tool is positioned as a new interface for Codex CLI supervision.
Inference The project appears to be a novel approach within the context of AI agent workflows, but there is no evidence of prior competition or market positioning.
Key Risks & Red Flags
- No commercial traction or adoption: The tool is described only as a hackathon prototype.
- No business model: There is no indication of monetization or commercial viability.
- Limited audience: It targets a niche group (Codex CLI users), which may limit scalability.
- Self-reported only: All claims are unverified and based on the author’s own experience.
Evidence
- No revenue, customers, or usage data provided.
- The tool is built for personal use, not commercial deployment.
- No mention of market demand or user feedback beyond the author.
Diligence Questions To Ask The Founders
- What is your actual workflow with Codex CLI? How many parallel sessions do you typically manage?
- Have you tested Vizhi with others, or is it purely a personal tool?
- Are there any plans to monetize or scale the product beyond the current prototype?
- What are the technical limitations of running this on localhost, and how might they affect scalability?
- How do you plan to integrate Vizhi with other AI agents or platforms beyond Codex CLI?
Investment/Partnership Verdict
There is no evidence of a commercial product, traction, or business model. The project appears to be a hackathon prototype built for personal use and does not demonstrate any signs of market demand or scalability.
Evidence
- No revenue, customers, or usage data.
- No indication of commercial viability or monetization strategy.
- The tool is self-reported as a personal development effort.
Inference At this stage, Vizhi is not a viable investment or partnership opportunity. It lacks the foundational elements of a product with traction or market potential.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.